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of Pittsburgh, University of Texas Medical Branch and BARDA, aimed at advancing pandemic bio-preparedness through AI-driven forecasting. With advances in machine learning frameworks and emerging accelerator
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data analysis/spectral image processing. Use of data analytics or machine learning to guide process design and optimization. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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. Develop advanced optimization, control, or machine learning strategies for distribution systems; validate these strategies using hardware-in-the-loop or real-time grid simulators. Develop optimization
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techniques in interfacial science; and mathematical techniques and computer programming for data analysis. Considerable skill in working interactively and productively in a multidisciplinary environment Good
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Demonstrated expertise in electrocatalysis, including catalyst synthesis, electrochemical characterization, and mechanistic analysis Hands-on experience with in situ/operando spectroscopy and/or advanced
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readout and controls (e.g., SQUID-based time- or microwave-multiplexed systems) with beamline data acquisition and control (EPICS/Bluesky). Develop and maintain data acquisition, calibration, and analysis
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, machine learning, and control in the energy sector. The postdoc researcher will perform theoretical study and algorithm development on optimization/control/data analytics methods and authorize peer-reviewed
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference